Chapter 1 Introduction 1 General Introduction

Computational chemistry, also referred to as molecular modeling, involves simulating chemical reactions and processes at the atomic level within the virtual space of a computer. This type of modeling is establishing itself as a valuable tool for chemists of all disciplines and this will only become more evident as computer technology continues to advance. In the pharmaceutical industry, for example, computational chemistry is considered an integral part of the drug design process and not just a novel academic endeavour. Despite the proven success of modeling, the application of it to studying transition metal based catalytic systems still presents a challenge. Part of the problem is that the simulation of reactions involving transition metals and extended ligand systems is still problematic with established methods.

One reasonable approach to escape this problem is the combined quantum mechanics and molecular mechanics (QM/MM) method.^7-9 In this hybrid method part of the molecular potential, such as the active site region, is determined by a quantum mechanical calculation while the remainder of the molecular potential is determined using a much faster molecular mechanics force field calculation. Such a partitioning is illustrated in Figure 1.1c. The promise of the QM/MM method is that it allows for simulations of bond breakage and formation at the active site, while still allowing for the role of the extended system to be modeled in an efficient and computationally tractable manner. The key feature of the QM/MM method is that the QM calculation is performed on a truncated "QM model"(Figure 1.1b) of the active site, where the large ligands have been removed and replaced with capping atoms. Then, a molecular mechanics calculation is performed on the remainder of the system and the effects of the attached ligands are incorporated to form the potential surface of the whole system where the QM and MM regions interact with one another via steric and electrostatic potentials.

The combined QM/MM methodology dates back to work by Warshel and Levitt⁷ in 1976, but it was not until 1986 that a practical QM/MM prescription was developed by Singh and Kollman.⁸ Despite its history, QM/MM methods have only recently¹⁰ received serious attention as a practical modeling tool to examine extended systems too large for pure QM methods.^8,9,11-19 Most applications have been concentrated in the areas of treating solvation ^10,18 and the simulations of proteins and nucleic acids.^12,20-24 In the arena of solvent simulations the combined QM/MM method has been shown to correctly model the charge reorganization of an active site due to the solvent and to provide accurate free energy barriers in the condensed phase.^10,14,25-27 The QM/MM modeling of proteins has also been successful despite the impressive challenges that these complicated macromolecules impose. An area which has only begun to be explored with hybrid QM/MM potentials is transition metal containing catalytic systems, such as metalloenzymes¹² and organometallic^28-30 complexes.

1.3 Ab initio Molecular Dynamics

Conventional electronic structure calculations can be classified as "static" simulations. In these calculations, reaction mechanisms are generally examined by optimizing stationary points (minima and transition states) on the potential surface at the zero temperature limit. There is no doubt that these studies are valuable since these stationary points play a central role in determining the kinetics and thermodynamics of a catalytic system. However, it is really the finite temperature free energy profile of a system that can be directly related to rate constants and thermodynamic properties derived from experiment.

Relative free energies of a chemical system can be mapped out at the molecular level using classical statistical thermodynamics. Here it is necessary to generate a series of configurations which sample a chosen statistical ensemble. The resulting ensemble averages provide the basis of the calculated thermodynamic properties. Computer simulations can be used to sample ensembles probabilistically using Monte Carlo (MC) sampling methods or deterministically by integrating Newton's equations of motion, via molecular dynamics(MD). MC and MD methods have a long and diverse history of studying chemical systems. They include simulations of liquid water,^31,32 proteins and nucleic acids.^33,34 Because of the enormous cost of properly sampling an ensemble, high level quantum mechanical methods have not traditionally been used. Instead, empirical molecular mechanics force fields have been utilized for these simulations because of their computational efficiency. As previously mentioned, the major drawback of molecular mechanics methods is that chemical reactions cannot be properly simulated and transition metal complexes are problematic. For these problem cases (and others), finite temperature free energy profiles have traditionally been calculated from conventional 'static' electronic structure calculations. The approach involves constructing an approximate partition function at each stationary point based on a harmonic frequency calculation. The 'static' approach to examining the finite temperature free energy surface is most efficient when reaction barriers are high and the potential surface has a high curvature.³⁴ However, when the potential surface is flat and the molecular system has a high degree of configurational variability, the static approach becomes inefficient and tedious to apply.

The potential energy surfaces of transition metal based catalyst systems, particularly those adept at mediating chemical transformations, can be flat and complicated. This often means that entropy and other finite temperature effects play an important role in determining the reactivity of the systems. Recently, the rapid advances in computational power have allowed molecular dynamics (and MC) techniques to be applied at the QM level. With this approach, which has been termed ab initio molecular dynamics, the potential surface is generated from a full electronic structure calculation as opposed to a molecular mechanics force field. This has allowed traditional, well-established molecular dynamics techniques to be applied to study chemical reactions and transition metal based systems.

The goal of this work is to apply and further develop the combined QM/MM and ab initio molecular dynamics methods so as to build more realistic computational models of catalytic reactions that take into account extended ligand effects, finite temperature effects and eventually solvation effects. Figure 1.1 nicely illustrates this objective starting from a 'real' chemical system, moving to more and more sophisticated computational models of it. The thesis contains both implementation and application components to it. In terms of implementation, the thesis describes the development and incorporation of the QM/MM method into both a conventional 'static' density functional package and into the framework of a Car-Parrinello ab initio molecular dynamics package. On the application side, we have examined the chemistry of several newly developed single-site olefin polymerization catalysts. (These transition metal based catalysts are expected to revolutionize the multi-billion dollar polyolefin industry.³⁵) In most cases, the studies in this thesis represent the first time these novel techniques have been applied to study transition metal based catalyst systems of this type. For this reason, we also hope to evaluate how these new methods can be effectively used to study the systems of interest. The ultimate goal of both the applications and implementation work is to help us gain a deeper understanding of the chemistry of single-site olefin polymerization systems and possibly contribute to the design of new catalyst systems.

Appended to this chapter, is a brief overview of the so called 'QM' and 'MM' methods for readers not familiar with the computational techniques. In Chapter Two we begin to discuss the QM/MM methodology in more detail. We also describe how we have adapted an existing QM/MM approach as to allow for both frequency calculations and molecular dynamics simulations with it. Details of the combined QM/MM implementation into the ADF density functional quantum chemistry package^36-39 are also presented in this chapter. Chapter Three details how the ADF QM/MM implementation has been applied to study the recently developed olefin polymerization catalyst systems of Brookhart¹ and McConville.⁴⁰ Chapter Four summarizes our experience with utilizing ab initio molecular dynamics as a practical tool to study homogenous catalysis. Next, in Chapter Five we present our implementation of the QM/MM method into the Car-Parrinello PAW⁴¹ ab initio molecular dynamics package. Here we also introduce and validate the multiple-time step QM/MM molecular dynamics methodology that we have developed. In Chapter Six we have applied both the ADF and PAW QM/MM programs to study the monomer capture process in olefin polymerization systems. Chapter Seven details the QM/MM PAW implementation to allow for eventual solvation simulations at the ab initio molecular dynamics level. The thesis is concluded in Chapter Eight with a summary of the work and an outlook.

Computational techniques have been used to predict anything from NMR spectra to the average shape of a solvated protein at 298 K. To study catalytic processes at the molecular level, it is necessary to model reaction energetics, dynamics and geometries. Here the molecular potential energy surface plays a fundamental role. There are a great variety of different methods available to calculate a system's potential energy surface. However, they can be roughly divided into two categories: quantum mechanical methods and molecular mechanics methods. Provided in this section is an elementary introduction to the methods.

The theoretical rigor of quantum mechanical based methods affords several advantages. In terms of studying catalysis, the most significant feature of QM based methods is that chemical reactions can be modeled at the molecular level. Thus, reaction intermediates, transition states and barriers can be determined and detailed reaction profiles can be elicited. From this, important kinetic and thermodynamic information of the catalytic processes can be determined. At the density functional level, reaction barriers can be estimated to within 15 kJ/mol.^60,61,64,65

The primary disadvantage of 'first principles' QM based methods is that their theoretical rigor also demands extensive computational resources. At the present time, detailed and expedient studies of molecular systems are limited to systems of under one hundred atoms. For this reason, calculations are generally performed on truncated model systems which can be a severe approximation to the real system. For example, in modeling single-site catalyst systems, the solvent and counter-ion are generally ignored.

Semi-empirical QM Methods. When the labour of an ab initio type calculation becomes too great, semi-empirical^66-68 methods are often employed. These methods have interesting acronyms such as CNDO,⁶⁹ INDO,⁷⁰ MINDO, ⁷¹, MNDO⁷², AM1,⁷³ and PM3.⁷⁴ With semi-empirical methods many approximations are made in solving the Schrödinger equation that are designed to speed up the calculations. To make up for these approximations, many empirical parameters are introduced that have been fitted to experimental or ab initio results. The fitted parameters that are introduced give these methods the name "semi-empirical".

Semi-empirical methods have gained wide acceptance by organic chemists and has developed into a useful tool for experimentalists. For modeling transition metal complexes, parameterization of semi-empirical methods^75,76 have been less successful. To date there is no method available that contains a single parameter set that provides both good geometries and energetics for these types of complexes. As a result, semi-empirical studies of transition metal complexes need to be augmented with higher level 'first principles' calculations in order to obtain meaningful results. As yet, for studying single site catalysts, semi-empirical methods are largely untested and therefore unreliable for mapping out potential energy surfaces.

One way of describing molecular mechanics is that it treats molecules as a set of balls connected together by springs. Each type of bond (e.g. C-C or C-H bond) is represented with a different kind of spring with a specific stiffness and equilibrium distance. If a bond is stretched, Hooke’s law is assumed which results in a restoring force and a rise in energy of the system. In this way, each type of bond possesses a unique potential surface that is characteristic of the bond’s natural length and strength. This is at the heart of molecular mechanics, the idea that a particular structural feature such as a C-H bond distance is essentially the same whether it is in butane or in DNA. In general, this is also true for bond angles, bond torsions, strain energies and so on. Thus, the structure and energy of large molecules can be formulated in terms of empirical parameters derived from the elementary features of smaller, well known molecules. Together, the sum of all the energy terms for the bond stretches (E_b), angles (E_), dihedrals (E_f), and non-bonded interactions (E_nb) of a molecule forms the potential energy surface.

E_T =  E_b +  E_ +  E_ +  E_nb

The functional forms of the various energy terms, and the parameters contained within them, make up what is called a force field. A force field is highly parameterized in order to provide reasonable agreement with experiment. Furthermore, the parameters are often fit to a specific group or type of molecular system and therefore force fields are generally designed to treat specific classes of molecules. For example, the AMBER⁷⁷ and CHARMM⁷⁸ force fields are designed to treat proteins and nucleic acids while the MMx⁷⁹ group of force fields are designed to treat small organic and main group inorganic molecules.

For studying transition metal based catalytic processes, molecular mechanics methods possess two serious limitations. First, bond breaking and formation of covalent bonds can not be treated and therefore chemical reactions cannot be simulated accurately. Secondly, force fields that can effectively deal with the complicated and varied bonding schemes of transition metals are only currently being developed.^80-82 These limitations confine molecular mechanics studies of transition metal catalysts to the qualitative regime. Studies of metallocene catalysts by molecular mechanics was pioneered by Corridini and co-workers.^83-86 These studies have contributed significantly to our understanding of the control of stereospecific -olefin polymerization by metallocene catalysts. Attempts to apply molecular mechanics to obtain quantitative or semi-quantitative results have been met with limited success.^58,87-91

Chapter 2
The QM/MM Methodology and Its Implementation into the ADF Quantum Chemistry Package
2.1 Introduction

The combined QM/MM approach involves partitioning the system into QM and MM regions whereby the molecular potential is determined partially by a quantum mechanical electronic structure calculation and partially by a molecular mechanics force field calculation. There have been many different approaches proposed to couple the two types of calculation in order to generate a single hybrid potential energy surface. In this chapter, a brief introduction to a few of the common QM/MM coupling schemes is given (At least those implemented in our ADF QM/MM program). We also introduce an adaptation of Morokuma's popular IMOMM¹⁵(Integrated Molecular Orbital and Molecular Mechanics) QM/MM coupling scheme in order to allow for practical normal-mode frequency calculations and molecular dynamics simulations to be performed with the method. Although the modification is simple, it allows for free energy surfaces to be investigated with the IMOMM method(via frequency and MD calculations). Following this, a summary of the QM/MM implementation into the ADF density functional package is given. Finally, we explore the applicability of utilizing the combined QM/MM method for performing frequency calculations and deriving thermochemical data. This aspect of the QM/MM hybrid potentials has only been previously explored to a limited extent.⁹²

Although the combined QM/MM method is conceptually simple, there are some substantial practical issues to contend with. This is particularly true, if the QM/MM partition occurs within the same molecule as illustrated in Figure 1.1c. Here, the difficulty lies in the fact that at least one covalent bond will involve an atom from the QM region and one from the MM region, and therefore the electronic system of the QM region must in some way be truncated as the QM/MM boundary is crossed along these bonds. A simple solution to this truncation problem first proposed by Singh and Kollman,⁸ involves capping the electronic system of the QM region with "capping" atoms. In this way, the electronic structure calculation is performed on a what is referred to as the QM model system, as shown in Figure 1.1b. We shall in the following refer to the 'real system' as the system consisting of all atoms (Figure 1.1a), QM or MM, not including the capping atoms. Compared to other truncation approaches,^7,93,94 the primary advantage of the capping atom approach is that no special treatment of the electronic system is necessary, thereby allowing the QM/MM methodology to be easily implemented within existing electronic structure codes. For this reason, we have opted to use Singh and Kolman's capping atom approach (and variations of it) in our QM/MM implementations.